We evaluate our framework on the KITTI dataset [1]}, a standard benchmark in the field providing both images and raw LiDAR depth maps obtained from 151 video sequences, as well as accurate ground-truth labels. Such annotation, based on semi-automatic procedures, is highly time-consuming and requires stereo images.
For instance, the KITTI completion dataset [2]} provides ground-truth maps obtained by accumulating 11 consecutive LiDAR pointclouds. Then, outliers (due to noise or moving objects) are removed by looking at inconsistency with respect to the output of the Semi-Global Matching (SGM) stereo algorithm [3]}. This labeling strategy allows generating massive data (about 44.5K samples, 93K if considering stereo pairs) with the side-effect of losing several labels where LiDAR and SGM are not consistent.
An even more accurate and laborious strategy consists of manually refining the labeling process, as done for the KITTI 2015 stereo dataset [4]}. In this case, 3D CAD models have been used to obtain an accurate annotation for cars at the cost of much more effort (indeed, only 200 annotated samples are available).
<TABLE>